




'''simple docstring'''



from typing import Dict

from .base import GenericTensor, Pipeline






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       def        UpperCamelCase_							(      self,							A=None,							A=None,							A=None,							**A  ):







             '''simple docstring'''







             if tokenize_kwargs is None:
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						{}

             if truncation is not None:
                   if "truncation" in tokenize_kwargs:
                         raise ValueError(
                             'truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'  )
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						truncation

             SCREAMING_SNAKE_CASE   :      int    					=						tokenize_kwargs

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						{}
             if return_tensors is not None:
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						return_tensors

             return preprocess_params, {}, postprocess_params







       def        UpperCamelCase_							(      self,							A,							**A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.framework
             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.tokenizer(A,							return_tensors=A,							**A  )
             return model_inputs







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Any    					=						self.model(**A  )
             return model_outputs







       def        UpperCamelCase_							(      self,							A,							A=False  ):







             '''simple docstring'''







             if return_tensors:
                   return model_outputs[0]
             if self.framework == "pt":
                   return model_outputs[0].tolist()
             elif self.framework == "tf":
                   return model_outputs[0].numpy().tolist()







       def __call__(      self,							*A,							**A  ):







             '''simple docstring'''







             return super().__call__(*A,							**A  )






'''simple docstring'''


from __future__ import annotations







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				if len(SCREAMING_SNAKE_CASE__	) <= 1 or n <= 1:
								return

				insert_next(SCREAMING_SNAKE_CASE__				, n - 1	)
				rec_insertion_sort(SCREAMING_SNAKE_CASE__				, n - 1	)







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				if index >= len(SCREAMING_SNAKE_CASE__	) or collection[index - 1] <= collection[index]:
								return

				# Swaps adjacent elements since they are not in ascending order
				_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			List[Any]							= (
				    collection[index],
				    collection[index - 1],
				)

				insert_next(SCREAMING_SNAKE_CASE__				, index + 1	)


if __name__ == "__main__":
				UpperCAmelCase_						:  List[str]				  =						input('Enter integers separated by spaces: ')
				UpperCAmelCase_						:  list[int]				  =						[int(num) for num in numbers.split()]
				rec_insertion_sort(number_list, len(number_list))
				print(number_list)




